The present disclosure relates to a method and apparatus for determining a license plate layout configuration. It is appreciated that the present exemplary embodiments are also amendable to other like applications.
An automatic license plate recognition (APLR) system is a vehicular surveillance system that captures images of moving or parked vehicles using a still or video camera. The system locates a license plate in the image, segments the characters in the plate, and uses optical character recognition (OCR) to determine the license plate number. The APLR system often functions as a core module for an intelligent transportation infrastructure system as its many uses can include monitoring traffic flow, enforcing traffic laws, and tracking criminal suspects.
Character segmentation is used to find the individual characters on the plate. Character segmentation presents multiple challenges to conventional APLR systems. One challenge is that jurisdictions may use different layouts, so the system must be able to recognize multiple layouts to be effective. Yet, at the same time, some jurisdictions use similar license plate protocols. For example, a number of states in the U.S. may group equal numbers of characters together between spaces and/or logos. A conventional system relies on a user for confirming that the license plate number correlates to the correct state.
Another challenge associated with character segmentation is the ability to distinguish characters from other objects that form part of the plate or obscure the plate. Screws, dirt, logos, graphics, plate borders, and plate frames can all affect the number of characters that the system detects on the plate. The objects can affect the boundaries drawn between segments and, thus, the ability for the system to distinguish the final output.
The effects of different imaging conditions also present a challenge to character segmentation. Overexposure, reflection, and shadows may result from low contrast, between the image and background, and poor or uneven lighting. There is a need for an ALPR system that is robust enough to handle different conditions.
There is a low tolerance for segmentation error made by an ALPR system because its many applications require high accuracy. Therefore, a need exists for an ALPR system that improves accuracy by reducing a risk of over- and under segmenting, determining erroneous results, and rejecting plate localization errors.